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As our society becomes more and more dependent on IT systems, failures of these systems can harm more and more people and organizations. Diligently performing risk and hazard analysis helps to minimize the potential harm of IT system failures on the society and increases the probability of their undisturbed operation. Risk and hazard analysis is an important activity for the development and operation of critical software intensive systems, but the increased complexity and size puts additional requirements on the effectiveness of risk and hazard analysis methods. This paper presents a qualitative comparison of two hazard analysis methods, failure mode and effect analysis (FMEA) and system theoretic process analysis (STPA), using case study research methodology. Both methods have been applied on the same forward collision avoidance system to compare the effectiveness of the methods and to investigate what are the main differences between them. Furthermore, this study also evaluates the analysis process of both methods by using a qualitative criteria derived from the technology acceptance model (TAM). The results of the FMEA analysis were compared to the results of the STPA analysis, which were presented in a previous study. Both analyses were conducted on the same forward collision avoidance system. The comparison shows that FMEA and STPA deliver similar analysis results.

Abstract. Defect taxonomies collect and organize the domain knowledge and project experience of experts and are a valuable instrument of system testing for several reasons. They provide systematic backup for the design of tests, support decisions for the allocation of testing resources and are a suitable basis for measuring the product and test quality. In this paper, we propose a method of system testing based on defect taxonomies and investigate how these can systematically improve the efficiency and effectiveness, i.e. the maturity of requirements-based testing. The method is evaluated via an industrial case study based on two projects from a public health insurance institution by comparing one project with defect taxonomy-supported testing and one without. Empirical data confirm that system testing supported by defect taxonomies (1) reduces the number of test cases, and (2) increases of the number of identified failures per test case.

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